Methods, media, and apparatus for optimizing physical training based on real-time blood lactate monitoring

ABSTRACT

Some embodiments described herein relate to receiving lactate concentration data from a non-invasive sensor. A ratio of aerobic to anaerobic exertion can be determined based on the concentration of lactate, and real-time feedback can be provided based on the ratio of aerobic to anaerobic exertion. For example, the user can be encouraged to maintain a level of intensity in which he or she approaches (but does not exceed) maximal oxygen consumption.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional U.S. PatentApplication No. 62/012,831, filed Jun. 16, 2014, under 35 U.S.C. §119(e), the disclosure of which is hereby incorporated by reference inits entirety.

BACKGROUND

This application relates generally to real-time monitoring of a user'sblood lactate (also referred to lactic acid) concentration during aphysical activity and providing training guidance to that user duringthe physical activity. Methods, media, and apparatus that can increasetraining efficiency by providing real-time feedback based on lactateconcentration are described herein.

“Getting fit” is a perennial New Year's resolution. The availability ofa wide range of exercise equipment, training aids, and fitness magazinesbears testament to the basic fact that some training methods are moreeffective than others. Even when an individual seeks to increase his orher fitness, the effectiveness of training can be compromised if theuser engages in a training regimen that was not specifically designedfor that individual and/or by deviations from the training protocol.Elite athletes have long benefited from coaches and training staffs thatcarefully monitor training regimens and adjust exertion goals based onperformance.

Training responses vary at different levels of intensity. Because thehuman body uses multiple energy systems for fueling output, includingaerobic and anaerobic metabolism, training efficiency can be improved bytargeting specific energy systems for specific durations. Elite andamateur athletes alike can improve the efficiency of their workouts byconforming their workouts to structured training regimens, which mayprescribe target intensity, target duration, and/or a sequence ofdifferent intensities.

Developing a structured training regimen for a particular individualand/or accurately conforming a workout to a structured training regimenrequires insight into the body's function. Several indicators can beused as proxies for measuring exertion, including heart rate, levels ofcarbon dioxide exhaled, and concentrations of lactate in the blood.Heart rate, however, is an unreliable indicator of exertion activitywith high latency. Carbon dioxide measurements require specializedexercise lab equipment. Lactate concentration is an excellent proxy forexertion, but has historically not been available in real-time and/orwithout drawing blood. A need therefore exists for methods, media, andapparatus for optimizing physical training based on real-time bloodlactate monitoring.

SUMMARY

Some embodiments described herein relate to receiving lactateconcentration data from a non-invasive sensor. A ratio of aerobic toanaerobic exertion can be determined based on the concentration oflactate, and real-time feedback can be provided based on the ratio ofaerobic to anaerobic exertion. For example, the user can be encouragedto maintain a level of intensity in which he or she approaches (but doesnot exceed) maximal oxygen consumption.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a system for providing real-timefeedback on physical training based on lactate concentration, accordingto an embodiment.

FIG. 2 is a flow chart of a method for providing training feedback basedon lactate concentration, according to an embodiment.

FIG. 3 is an example chart of various target training zones.

FIG. 4 is an example chart of a workout summary.

DETAILED DESCRIPTION

Mammals have multiple energy systems, including aerobic metabolism andanaerobic metabolism. When muscles are well oxygenated, theypreferentially produce energy via aerobic metabolism. As the intensityof a physical activity increases, the supply of oxygen drops and alarger portion of the energy produced to engage in the physical activityis generated via anaerobic activity, a byproduct of which is lactate. Asthe intensity of physical activity decreases, well oxygenated musclesquickly oxidize lactate to form pyruvate, decreasing lactateconcentration. Blood lactate concentration is therefore an excellentindicator of the current ratio of aerobic to anaerobic metabolismoccurring in a mammalian body.

Some embodiments described herein relate to receiving lactateconcentration data from a non-invasive sensor. A ratio of aerobic toanaerobic exertion can be determined based on the concentration oflactate, and real-time feedback can be provided based on the ratio ofaerobic to anaerobic exertion. For example, the user can be encouragedto maintain a level of intensity in which he or she approaches (but doesnot exceed) maximal oxygen consumption.

Some embodiments described herein relate to an apparatus having an inputmodule that can be receive data from a non-invasive lactateconcentration sensor. A training module can be coupled to the inputmodule and configured to calculate an exertion level based on thelactate concentration. An output module can be communicatively coupledto the training module and configured to cause an output device toprovide training feedback while a user is engaged in physical activity.

Some embodiments described herein relate to classifying a training zonefor a physical activity based on a lactate concentration while thephysical activity is occurring. If the training zone is higher than atarget training zone, the user can be prompted, in-real time, to reducean intensity of the physical activity. Conversely, if the training zoneis lower than the target zone, the user can be prompted to increaseintensity of the physical activity.

The efficiency of training can be improved by targeting exercising toprecise intensity levels to systematically engage different energysystems, sometimes referred to as “zone training.” For example, manyathletes practice interval training, in which relatively long periods ofrelatively low intensity aerobic exercise are punctuated by relativelyshort high-intensity exercises designed to recruit fast-twitch musclesand engage anaerobic metabolic pathways.

FIG. 3 shows relationships between notional training zones and lactateconcentrations, which are highly correlated to the ratio of aerobic toanaerobic metabolism occurring within the body. Historically, zonetraining has relied on heart rate. Heart rate, however, is onlyindirectly and non-linearly correlated to the ratio of aerobic toanaerobic exercise. Because lactate is a direct product of anaerobicmetabolism, it is a better indicator of how much anaerobic metabolism isoccurring than heart rate. Effective training regimens based onreal-time lactate concentration, however, have not previously beendeveloped.

As described herein, “zone 2,” is associated with oxygen uptake and veryhigh ratios of aerobic to anaerobic metabolism. Once an athlete crossesthe “aerobic threshold” into “zone 3,” which, in humans, is typicallyassociated with a blood lactate concentration of about 2.0millimoles/liter (e.g., 1.8-2.2 millimoles/liter), muscles begin tosupplement energy production demands with anaerobic metabolism.Therefore, maximizing an intensity of a physical activity withoutcrossing the threshold into zone 3 causes muscles to function at nearpeak aerobic power. This is a highly efficient training strategy. Oncean athlete transitions to zone 3, the effect of relatively low levels ofanaerobic metabolism allows muscles to perform at less than peak aerobiccapacity. Therefore, although the overall intensity of an exerciseincreases as the athlete transitions from zone 2 to zone 3,counterintuitively, training efficiency decreases. Accordingly, in someembodiments, training regimens described herein can target the “top” ofzone 2. Similarly stated, for at least a portion of a workout, anathlete can be encouraged to maintain at an intensity close to, withoutexceeding the aerobic threshold. Similarly, highly efficient trainingcan occur at the top of other suitable zone, such as at the top of zone4, before the body crosses the “anaerobic threshold,” which, in humans,is typically associated with a blood lactate concentration of about 4.0millimoles per/liter (e.g., 3.8-4.2 millimoles/liter). Accordingly, insome embodiments, training regimens described herein can target the“top” of any suitable zone.

As shown in FIG. 3, heart rate can also be correlated to a trainingzone, but heart rate is generally a less reliable predictor of the ratioof aerobic to anaerobic exercise. In particular, heart rate is poorpredictor of the threshold between zones 2 and 3. Therefore, it hashistorically not been possible to provide effective real-time trainingfeedback to athletes wishing to target zone 2. Lactate, being a directproduct of anaerobic metabolism, is an excellent indicator of thetransition between zones 2 and 3.

Some embodiments described herein relate to targeting training intensityto maximize aerobic metabolism and minimize anaerobic metabolism.Because there are few or no cues that are perceptible to the athletewhen low-level anaerobic metabolism begins to occur, unguided athletesattempting to engage in aerobic activity will frequently exercise at anintensity level that causes a relatively small but significant amount ofanaerobic metabolism to occur. The occurrence of such anaerobicmetabolism can be counterproductive to aerobic training. Knownheart-rate monitor based training programs are ineffective at detectingthe presence of such anaerobic metabolism. The lactate-based systems andmethods described herein are sensitive to relatively small amounts ofanaerobic exertion and are therefore suitable for increasing theefficiency of training, for example, by encouraging a user to maximizeaerobic exercise without engaging significant anaerobic metabolism.Other training regimens described herein include targeting particularratios of aerobic to anaerobic exercise for a particular duration orpercentage of a workout and/or targeting particular zones in aparticular order.

FIG. 1 is a schematic block diagram of a system for providing real-timefeedback on physical training based on lactate concentration, accordingto an embodiment. The system includes a training device 110communicatively coupled to a sensor 120. The training device 110 can becommunicatively coupled to the sensor 120 via a wired connection and/ora wireless connection. In some embodiments, the training device 110 andthe sensor 120 can be enclosed within a single housing, such as awearable compute device. In other embodiments, the training device 110and the sensor 120 can be stand-alone components communicatively coupledvia USB®, Bluetooth®, or any other suitable communication mechanismand/or protocol. For example, and as described in further detail herein,the training device 110 can be a smart phone and the sensor 120 can be abody-worn device and/or incorporated into a clothing item.

The sensor 120 can be any suitable device for measuring blood lactateconcentration. In some embodiments, the sensor 120 can be operablemeasure blood lactate concentration and send data associated with bloodlactate concentration to the training device 110 in real-time. Similarlystated, the sensor 120 can be operable to detect and/or report lactateconcentrations in the blood of a subject while the subject is performinga physical activity and/or report the lactate concentrations to thetraining device with substantially no delay (e.g., with a delay of lessthan 5 seconds, less than 1 second, less than 5 milliseconds, etc.).

The sensor 120 includes a processor 122, a memory 124, and a detectionmodule 126. The detection module 126 can be any suitable hardware and/orsoftware (stored in the memory 124 and/or executing on the processor122) for measuring lactate and/or lactic acid concentrations and/orquantity. In some embodiments the entire sensor 120, including thedetection module 126 can be non-invasive and/or may not require a bloodsample. For example, the detection module 126 can be operable totransdermally detect lactate concentration using electropotentialmeasurements (or other electromagnetic measurements) and/or opticalmeasurements (e.g., by detecting photons (directly or as reflected) inone or more spectra such is visible light, near-infrared light, orinfrared light).

The processor 122 can be for example, a general purpose processor, aField Programmable Gate Array (FPGA), an Application Specific IntegratedCircuit (ASIC), a Digital Signal Processor (DSP), and/or the like. Theprocessor 122 can be configured to retrieve data from and/or write datato memory, e.g., the memory 124, which can be, for example, randomaccess memory (RAM), memory buffers, hard drives, databases, erasableprogrammable read only memory (EPROMs), electrically erasableprogrammable read only memory (EEPROMs), read only memory (ROM), flashmemory, hard disks, floppy disks, cloud storage, and/or so forth.

The training device 110 can be a smart phone (e.g., an iPhone®, anAndroid® phone, etc.), a personal computer (e.g., a desktop, laptop,tablet, etc.) and/or any other suitable computing entity (e.g., customhardware and/or software configured to provide training feedback). Insome embodiments, the training device 110 can be carried and/or worn bythe user. In other embodiments, the training device can be incorporatedinto athletic equipment, such as a running shoe, a ski, an oar, etc. Inyet other embodiments, the training device 110 can be a remote computingentity configured to receive telemetry from the sensor 120 over along-distance network, such as a cellular data network, the internet,etc. In some embodiments, the training device 110 can be used by a coachor trainer to monitor athletic activities performed by the wearer of thesensor 120.

The training device 110 includes a processor 112 and a memory 114, whichcan be functionally and/or structurally similar to the processor 122and/or the memory 124, respectively. The training device 110 furtherincludes an analysis module 115, a training module 116, a comparisonmodule 117, and an output module 118.

The analysis module 115 can be hardware and/or software (stored in thememory 114 and/or executing on the processor 112) operable to receiveand/or process lactate and/or lactic acid data received from the sensor120. The analysis module 115 can be operable to receive and/or processlactate data in real time (e.g., while a physical activity is underwayand/or with a delay of a delay of less than 5 seconds, less than 1second, less than 5 milliseconds, etc.) and/or substantiallycontinuously (e.g., updating every 10 seconds, every 5 seconds, every 1second, every 10 milliseconds, etc.).

As described in further detail herein, the analysis module 115 can beoperable to associated lactate data with an intensity of the physicalactivity. For example, the analysis module 115 can be operable tocorrelate lactate concentration to a quantity of anaerobic metabolismand/or a ratio of aerobic to anaerobic exercise. The analysis module 115can assign a “zone” to the current activity based on lactateconcentration, for example, based on the lactate concentrations shown inFIG. 3.

In some instances, the analysis module 115 can be operable to receiveheart rate, respiration data, CO₂ exhalation data, and/or any othersuitable physiological data (e.g., from the sensor 120 and/or any othersuitable physiological sensor) and can assign a zone based on lactateconcentration and any other suitable physiological data. For example,the analysis module 115 can be operable to distinguish between zones 1and 2, which are both associated with very high ratios of aerobic toanaerobic exertion, based, at least in part, on heart rate.

The training module 116 can be hardware and/or software (stored in thememory 114 and/or executing on the processor 112) operable to defineworkout regimens for the user of the training device 110 and/or storetraining regimens (e.g., in the memory 114). For example, the trainingmodule 116 can be obtain and/or store pre-defined generic trainingregimens, for example from an internet repository. In some instances,the training module 116 can be operable to define one or more customtraining regimens for the user based, for example, on the user's goals,and/or current and/or historic exercise data for the user.

The comparison module 117 can be hardware and/or software (stored in thememory 114 and/or executing on the processor 112) operatively coupled tothe analysis module 115 and the training module 116, and can be operableto determine whether a current training zone (e.g., as determined by theanalysis module 115) corresponds to a target zone. For example and asdescribed in further detail herein, the training regimen (defined by thetraining module 116) can prescribe exercising in zone 2 for a period oftime. Similarly, the training regimen can prescribe increasing theintensity to zone 4 (or any other suitable zone) at a particular timeand for a particular duration (e.g., after 7 minutes of zone 2 exertion,45 seconds of zone 4 exertion can be prescribed). As yet anotherexample, the training regimen can prescribe that at least 85% of aworkout should be at zone 2 intensity. The comparison module 117 can beoperable to verify compliance with any and/or all such prescriptions.For example, the comparison module 117 can be operable to comparecurrent exertion to current prescribed intensity, track time and historyof an exercise, and/or verify that the user's intensity level changeswhen called for by the training regimen, etc.

The comparison module 117 can be operably coupled to the output module115. The output module can be hardware and/or software (e.g., stored inmemory 114 and/or executing on the processor 112) operable to causeuser-perceptible signals to be generated. For example, the output module115 can be operable to cause a visual output to be presented via adisplay device (e.g., a LCD screen, an e-ink display, etc.) an audibleoutput to be presented via a speaker or other suitable device, a haptic,and/or any other suitable output to be presented. As described infurther detail herein, the output module 115 can be operable to causecues, feedback, encouragement, tracking data, and/or any other suitableinformation to be presented to the user to aid in adhering to thetraining regimen. For example, the output module 115 can be operable toencourage the user to increase and/or decrease the intensity of aphysical activity when the user is not adhering to the training regimen.

FIG. 2 is a flow chart of a method for providing training feedback basedon lactate concentration, according to an embodiment. At 210 a trainingregimen can be defined, for example, by the training module 116 shownand described above with reference to FIG. 1. The training regimen canspecify the amount of time and/or the percent of time the athlete shouldstay in a particular training zone measured over the course of aworkout, a week, a month, and/or a year. The prescribed amount and/orpercent of time can be determined by an analysis of the athlete'srelative strengths, the athlete's goals (e.g., lose a certain amount ofweight, improve the time taken to complete an activity, etc.), an eventfor which the athlete is training (e.g., a race, a match, a game, etc.),the amount of time until the event, and so forth.

In some instances, the training regimen can be based on generallyapplicable zone thresholds, for example, as shown in FIG. 3. In otherinstances, the training regimen can be based on personalized zonethresholds. For example, defining the training regimen, at 210 caninclude one or more calibration exercises. As one example, the user canbe encouraged to slowly increase the intensity of an exercise. As theuser begins exercising, the analysis module 116 can define a baselinelactate concentration. As the intensity increases, the analysis module116 can monitor the lactate concentration. When the lactateconcentration deviates from the baseline lactate concentration, (e.g.,when an inflection point in a lactate concentration curve is detected)the training module 116 can define the aerobic threshold, which istypically around 2.0 millimoles/liter. This personalized aerobicthreshold can be used as the zone 2-zone 3 threshold for trainingregimens described in further detail herein. As another example,defining the training regimen, at 210, can include a calibrationexercise designed to detect a personalized anaerobic threshold. Theanaerobic threshold can be detected during a high-intensity exercise andcan be associated with another change in slope or inflection point oflactate concentration indicative of lactate being produced by musclesengaged in anaerobic metabolism faster than the rest of the body canmetabolize the produced lactate, which is typically around 4.0milimoles/liter. This personalized anaerobic threshold can be used asthe zone 4-zone 5 threshold for training regimens described in furtherdetail herein. In some instances, calibration exercises can be repeatedto confirm and/or improve the accuracy of the individualized aerobicthreshold and/or anaerobic thresholds.

In some instances, defining a training regimen can include accessing adatabase (e.g., a local database, a remote database, a third partyrepository, crowd sourced data, etc.) of previously generatedquantitative training programs for various events, distances, traininggoals, timeframes, etc. For example, In other instances, defining atraining regimen can include defining a custom training regimen basedon, for example, past records of physical activity, user-supplied goals,user-expressed training preferences, etc. Defining a training regimencan include calculating daily, weekly, monthly, and/or yearly trainingvolume using, for example, principles of progressive loading andperiodization, i.e., gradually increasing volume by week and by month,with rest weeks, and varying the types of zones appropriately withineach of those cycles. This variation in types of zone can furtherinclude instruction to the user on the amounts of time spent in eachzone, time spent in the aggregate, and the order in which the trainingzones are targeted. This time spent (and/or order spent in the zones)are applicable over the course of a single exercise session, and alsoacross multiple/many sessions measured across timespans measured indays, weeks, and/or months.

Thus, in some instances, defining a training regimen, at 210 can includedefining a long-term training regimen and/or selecting an individualworkout from a long term training regimen. In other instances, defininga training regimen, at 210 can include defining a one-off trainingregimen for a single exercise session.

An example of a training regimen is shown in the “Goal” columns of FIG.4. Such a training regimen can further include a specific sequence ofintensities. For example, a workout may prescribe 18 minutes of zone 1warm-up, followed by four repetitions of (1) 5 minutes of zone 2activity, (2) 1.5 minutes of zone 4 high intensity activity, (3) 4minutes of zone 1 cool down.

At 220, real-time lactate data can be received. Similarly stated, thetraining device 110 can receive a signal from a sensor 120, as shown anddescribed above with reference to FIG. 1. In some instances, lactateconcentration data can be received, at 220, in response to the userbeginning a workout and/or in response to the user providing an input tothe sensor 120 and/or the training device 110 indicating that the useris commencing a physical activity. In response to the lactate data beingreceived, at 210, the analysis module 115 can be operable to determine aquantity of anaerobic metabolism and/or a ratio of aerobic to anaerobicmetabolism, in real-time and/or substantially continuously, at 220. Thelactate concentration received, at 220, (and hence the ratio of aerobicto anaerobic metabolism determined, at 230) can be suitable to provideleading indicators predictive of certain conditions such as (but notlimited to) overtraining and injury before they happen.

Compliance with the training regimen can be determined, at 240. At 240,the comparison module 117 can, in real time and/or substantiallycontinuously, compare lactate concentration received at 220 and/or theratio of aerobic to anaerobic metabolism, determined, at 230, to thetraining regimen defined, at 210. For example, if the training regimencalls for the user to currently be exercising in zone 2, but the user isactually exercising with zone 3 intensity, the compliance module 117 candetermine that the user is not complying with the training regimen, at240. Similarly, if the training regimen calls for 8 minutes of zone 4activity over the course of a workout (which may last, for example, for45 minutes), the compliance module 117 can be operable to track thecumulative amount of time spent in zone 4 and can be operable todetermine when the user exceeds 8 minutes of zone 4 activity. As yetanother example, if the training regimen calls for 180 minutes of zone 2activity over the course of a week, the compliance module 117 can beoperable to determine, at 240, if the user is on track to meet thetarget goal for the week.

In some instances, determining compliance with the training regimen, at240, can include determining if the user is exercising near the top ofthe prescribed training zone. For example, if the training regimen callsfor the user to currently be exercising in zone 2, the compliance module117 can determine how close the user is to the aerobic threshold, andcan encourage the user to exercise with an intensity that approaches,but does not exceed, the aerobic threshold.

At 250, feedback can be provided, in real time and/or substantiallycontinuously, based on compliance or non-compliance with the trainingregimen. For example, the comparison module 117 can be operable to senda signal to the output module 118 such that an output device alerts theuser to compliance status. For example, the user can be encouraged toincrease intensity of a physical activity if the current activityintensity is below the intensity prescribed by the training regimen,decrease intensity if the current activity intensity is above theintensity prescribed by the training regimen, encourage the user tomaintain an intensity if the current activity is within the targetintensity, notify the user of progress towards a goal (e.g., “5 moreminutes at this intensity”), and so forth. In some instances, feedbackto increase an intensity of an exercise can be provided, at 250, untilthe compliance module 117 indicates that the user has left theprescribed zone. Additional feedback can then be prescribed, at 250, toslightly decrease intensity. In this way, in some instances, the usercan be encouraged to exercise at the top of the prescribed zone.

In some instances, compliance with the training regimen can bedetermined, at 240, and feedback can be provided, at 250, based solelyon lactate concentration. In other instances, compliance with thetraining regimen can be determined, at 240, and feedback can beprovided, at 250, based on any number of parameters, such as lactateconcentration, heart rate, respiration, galvanic skin response,altitude, duration of an activity, terrain (hills), etc.

Any suitable feedback can be provided at 250. For example, musical cuescan encourage a user to increase, decrease, or maintain an intensity byvarying the tempo, volume, frequency, etc. of a composition. Visualcues, such as stop-light style displays can be presented to encourage auser to maintain or alter an intensity of an activity. Charts, graphs,or other data visualization can provide feedback. Spoken phrases, suchas “speed up,” “slow down,” “begin sprint,” “begin recovery fromsprint,” “5 more minutes at this intensity,” “you're doing great,”“you've done better,” “just a little faster,” and so forth, can beprovided. Haptic feedback, such as metronomic pulsing can be provided.

In some instances, feedback can be provided based on challengesresponsive to difficulties commonly experienced by athletes engaging inthe type of training regimen defined, at 210. For example, in theinitial stages of zone training, optimal training may be lower intensitythan the user may be accustomed; a challenge can be intended to informthe user to decrease intensity. In other instances, challenges can becustomized or selected for a user based on, for example, personalpreferences, training regimen, etc. Feedback can be stored and/or tagged(e.g., in a database) by type and/or the desired effect—for instance forencouragement or to more directly challenge.

Feedback can be provided, at 250, based on preselected or preferences ofthe user. For example, the user can preselect along a spectrum of his orher desired ratio of positive versus negative reinforcement to which heor she responds. Coaching feedback can be probabilistically selectedaccording to these ratios. Users can vote on their favorite sayings. Forexample, an up vote can increase the probability that a motivationalmessage will be repeated, while a down vote can decrease the probabilitythat the motivational message will be repeated, and/or to reject themessage from being appearing in the future.

In some instances, feedback can be provided, at 250, after a trainingactivity in addition or alternative to real-time feedback. For example,a workout summary can be presented at the conclusion of and/or during anexercise, such as the example workout summary shown in FIG. 4. Historicworkout data can be used to predict performance for a future workout orevent, to establish pacing, analyze progress. Historic data can furtherbe used to detect deviations from past performance, for example during aworkout. For instance, if a user is unable to maintain a pace that he orshe has previously set, it can be an indication of a mental lapse.Feedback can be provided to the user in the event such a deviationoccurs encouraging the user to increase his or her pace. If the userdoes not increase his or her pace, it can be an early indication ofinjury or overtraining, and training regimens can be adjusted and/orredefined.

In some instances, during or at the conclusion of a workout, trainingregimes can be updated or defined for future workout sessions based onthe user's performance and/or compliance with the training regimendefined at 210. For example, if a workout is completed with a highdegree of compliance to the prescribed regimen, in some instances, amore challenging workout can be defined for a future session.Conversely, if a workout is completed with a low degree of compliance tothe prescribed regimen, a less challenging workout can be defined for afuture session. As another example, after a low-intensity, long-durationexercise is completed, long-term training regimen can be updated suchthat the next workout includes speed work or intervals such that thelong term training regimen includes a variety of different exercisestargeting different muscle adaptations.

While various embodiments have been described herein, it should beunderstood that they have been presented by way of example only, and notlimitation. For example, although some embodiments described hereinrelate to providing feedback to a user while the user exercises, inother embodiments, feedback can be provided to a coach, trainer, and/orother individual. Furthermore, although embodiments are generallydescribed within the context of a human athlete being monitored andprovided feedback, it should be understood that that systems, methods,and apparatus described herein can be applied to any mammalian animal.For example, lactate of a racing greyhound can be detected and real-timeand/or post-workout feedback can be provided to trainer. The trainer canencourage the dog to increase and/or decrease its pace (e.g., increaseor decrease the speed of a lure) based on lactate concentration.

As another example, although embodiments generally describe providingtraining feedback based on lactate concentration, it should beunderstood that such training feedback may be based on any suitabledata. That is, the training device 110 and/or sensor 120 can be operableto detect and/or provide feedback based on any suitable physiological orother feature. For example, the sensor 120 can be operable to measureheart rate, respiration rate, galvanic response, etc. The trainingdevice 110 may store or have access to GPS data, map data, past activitydata, sleep data, diet information, travel information, etc. Such datacan be combined in any suitable fashion to provide feedback. As anexample, the training device 110 can be operable to detect that the useris approaching a hill based on GPS and/or map data. The training device110 can encourage the user to conduct a hill sprint based on suchinformation. Alternatively, the training device 110 may encourage theuser to conserve energy leading up to a hill based on, for example, theuser's heart rate being higher than the user's heart rate has been inthe past leading up to the hill.

Furthermore, although various embodiments have been described as havingparticular features and/or combinations of components, other embodimentsare possible having a combination of any features and/or components fromany of embodiments where appropriate as well as additional featuresand/or components.

Where methods described herein indicate certain events occurring incertain order, the ordering of certain events may be modified.Additionally, certain of the events may be performed repeatedly,concurrently in a parallel process when possible, as well as performedsequentially as described above. Furthermore, certain embodiments mayomit one or more described events. Where methods are described, itshould be understood that such methods can be computer-implementedmethods. Similarly stated, a non-transitory processor readable mediumcan store code representing instructions configured to cause a processorto cause the described method to occur or be carried out.

Some embodiments described herein relate to computer-readable medium. Acomputer-readable medium (or processor-readable medium) isnon-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) may bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to: magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asASICs, PLDs, ROM and RAM devices.

Examples of computer code include, but are not limited to, micro-code ormicro-instructions, machine instructions, such as produced by acompiler, code used to produce a web service, and files containinghigher-level instructions that are executed by a computer using aninterpreter. For example, embodiments may be implemented using Java,C++, or other programming languages (e.g., object-oriented programminglanguages) and development tools. Additional examples of computer codeinclude, but are not limited to, control signals, encrypted code, andcompressed code.

What is claimed is:
 1. A non-transitory processor readable mediumstoring code representing instructions to be executed by a processor,the code comprising code to cause the processor to: receive, from anon-invasive sensor, a signal associated with a direct measurement of aconcentration of lactate in a blood stream of a mammalian body;determine a ratio of aerobic to anaerobic exertion based on the directmeasurement of the concentration of lactate; compare the ratio ofaerobic to anaerobic exertion to an aerobic threshold and an anaerobicthreshold; and send a signal to an output device to cause the outputdevice to provide feedback during a training activity based on aproximity of the ratio of aerobic to anaerobic exertion to at least oneof the aerobic threshold or the anaerobic threshold.
 2. Thenon-transitory processor readable medium of claim 1, wherein thefeedback includes an instruction to the user to reduce an intensity of aphysical activity based on the ratio of aerobic to anaerobic exertionexceeding the aerobic threshold.
 3. The non-transitory processorreadable medium of claim 1, wherein: the ratio of aerobic to anaerobicexertion is determined based solely on the direct measurement of theconcentration of lactate; and the feedback includes an instruction tothe user to reduce an intensity of a physical activity based solely onthe comparison of the ratio of aerobic to anaerobic exertion to theaerobic threshold and the anaerobic threshold.
 4. The non-transitoryprocessor readable medium of claim 1, wherein the non-invasive sensor isa first non-invasive sensor and concentration of lactate is a firstphysiological parameter, the code further comprising code to cause theprocessor to: receive, from a second non-invasive sensor, a signalassociated with a second physiological parameter different from thefirst physiological parameter, the code to cause the processor todetermine the ratio of aerobic to anaerobic exertion includes code tocause the processor to determine the ratio of aerobic to anaerobicexertion based on the direct measurement of the concentration of lactateand the second physiological parameter.
 5. The non-transitory processorreadable medium of claim 1, wherein the feedback includes an instructionto increase an intensity of a physical activity based on theconcentration of lactate.
 6. The non-transitory processor readablemedium of claim 1, wherein the ratio of aerobic to anaerobic exertion isdetermined based solely on the direct measurement of the concentrationof lactate.
 7. The non-transitory processor readable medium of claim 1,wherein the ratio of aerobic to anaerobic exertion is a first ratio, thecode further comprising code to cause the processor to: define atraining regimen prescribing performing a physical activity performed ata first intensity level for a first duration and the physical activityperformed at a second intensity level for a second duration, the firstintensity level being below the aerobic threshold and associated with asecond ratio of aerobic to anaerobic exertion associated with oxygenuptake, the second intensity level being below the anaerobic thresholdand associated with a third ratio of aerobic to anaerobic exertion lowerthan the second ratio, the third ratio associated with high intensityendurance; and record the first ratio and a third duration associatedwith the first ratio.
 8. The non-transitory processor readable medium ofclaim 7, the code further comprising code to cause the processor to:send a signal to the output device to cause the output device to presentan instruction to reduce an intensity of the physical activity based onthe first ratio being less than the second ratio and the third durationbeing greater than the first duration.
 9. The non-transitory processorreadable medium of claim 1, wherein the ratio of aerobic to anaerobicexertion is a first ratio, the code further comprising code to cause theprocessor to: define, at a first time, a training regimen prescribing:(1) performing a physical activity at a first intensity level associatedwith a second ratio of aerobic to anaerobic exertion, the second ratioof aerobic to anaerobic exertion being below the aerobic threshold, (2)performing the physical activity at a second intensity level associatedwith a third ratio of aerobic to anaerobic exertion, the third ratio ofaerobic to anaerobic exertion being above the first ratio of aerobic toanaerobic exertion and below the anaerobic threshold, and (3) avoidingperforming the physical activity at a third intensity level between thefirst intensity level and the second intensity level, the signalassociated with the direct measurement of the concentration of lactatereceived at a second time after the first time; and send, after acompletion of the physical activity, a signal to the output device tocause the output device to present an indication of compliance with thetraining regimen.
 10. The non-transitory processor readable medium ofclaim 1, wherein the ratio of aerobic to anaerobic exertion is a firstratio, the code further comprising code to cause the processor to:define, at a first time, a training regimen prescribing (1) performing aphysical activity at a first intensity level associated with a secondratio of aerobic to anaerobic exertion below the aerobic threshold, (2)performing the physical activity at a second intensity level associatedwith a third ratio of aerobic to anaerobic exertion above the aerobicthreshold and below the anaerobic threshold, and (3) avoiding performingthe physical activity at a third intensity level between the firstintensity level and the second intensity level, the signal associatedwith the direct measurement of the concentration of lactate received ata second time after the first time; and send a signal to the outputdevice to cause the output device to provide feedback to alter anintensity of the physical activity based on the ratio of aerobic toanaerobic exertion determined based on the direct measurement of theconcentration of lactate being associated with the third intensitylevel.
 11. The non-transitory processor readable medium of claim 1,wherein the code to cause the processor to determine the ratio ofaerobic to anaerobic exertion includes code to cause the processor todetermine the ratio of aerobic to anaerobic exertion substantiallycontinuously during a performance of a physical activity based on thesignal being received from the non-invasive sensor substantiallycontinuously.
 12. The non-transitory processor readable medium of claim1, wherein the ratio of aerobic to anaerobic exertion is a first ratio,the code further comprising code to cause the processor to: define atraining regimen prescribing performing a physical activity at anintensity level associated with a second ratio, the second ratio beingless than the first ratio, the feedback including an instruction toincrease an intensity of a physical activity to approach the intensitylevel without exceeding the intensity level.
 13. The non-transitoryprocessor readable medium of claim 1, wherein the ratio of aerobic toanaerobic exertion is a first ratio determined during a first timeperiod, the code further comprising code to cause the processor to:define a training regimen prescribing (i) performing a physical activityat a first intensity level associated with a second ratio below theaerobic threshold during the first time period and (ii) performing thephysical activity at a second intensity level associated with at a thirdratio greater than the second ratio during a second time period, thefeedback including an instruction to increase an intensity of thephysical activity to approach the first intensity level withoutexceeding the first intensity level.
 14. The non-transitory processorreadable medium of claim 1, wherein the ratio of aerobic to anaerobicexertion is a first ratio received during a second time period, the codefurther comprising code to cause the processor to: define a trainingregimen prescribing (i) performing a physical activity at a firstintensity level associated with a second ratio below the aerobicthreshold during a first time period and (ii) performing the physicalactivity at a second intensity level associated with a third ratiogreater than the second ratio during the second time period, thefeedback including an instruction to increase an intensity of thephysical activity.
 15. The non-transitory processor readable medium ofclaim 1, wherein the signal associated with the direct measurement ofthe concentration of lactate is from a plurality of signals representinga plurality of direct measurements of the concentration of lactate, thecode further comprising code to cause the processor to: define theaerobic threshold based on a first inflection point in the plurality ofdirect measurements of the concentration of lactate; and define theanaerobic threshold based on a second inflection point in the pluralityof direct measurements of the concentration of lactate.
 16. Thenon-transitory processor readable medium of claim 1, wherein the signalassociated with the direct measurement of the concentration of lactateis a first signal associated with a first direct measurement of a firstconcentration of lactate from a plurality of signals representing aplurality of direct measurements of the concentration of lactate, thecode further comprising code to cause the processor to: define theaerobic threshold based on a first inflection point in the plurality ofdirect measurements of the concentration of lactate; and define theanaerobic threshold based on a second inflection point in the pluralityof direct measurements of the concentration of lactate, the ratio ofaerobic to anaerobic exertion determined based on the firstconcentration of lactate relative to at least one of the aerobicthreshold and the anaerobic threshold.
 17. The non-transitory processorreadable medium of claim 1, wherein the signal associated with thedirect measurement of the concentration of lactate is a first signalassociated with a first direct measurement of a first concentration oflactate from a plurality of signals representing a plurality of directmeasurements of the concentration of lactate, the code furthercomprising code to cause the processor to: define the aerobic thresholdbased on a first inflection point in the plurality of directmeasurements of the concentration of lactate; and define the anaerobicthreshold based on a second inflection point in the plurality of directmeasurements of the concentration of lactate, the ratio of aerobic toanaerobic exertion determined based on the first concentration oflactate relative to the aerobic threshold and the anaerobic threshold.18. The non-transitory processor readable medium of claim 1, wherein:the signal associated with the direct measurement of the concentrationof lactate is from a plurality of signals representing a plurality ofdirect measurements of the concentration of lactate; and the ratio ofaerobic to anaerobic exertion is a first ratio, the code furthercomprising code to cause the processor to: define the aerobic thresholdbased on a first inflection point in the plurality of directmeasurements of the concentration of lactate; define the anaerobicthreshold based on a second inflection point in the plurality of directmeasurements of the concentration of lactate; and define a trainingregimen prescribing: (1) performing a physical activity at a firstintensity level that does not exceed the aerobic threshold, (2)performing the physical activity at a second intensity level that doesnot exceed the anaerobic threshold, and (3) avoiding performing thephysical activity at a third intensity level that exceeds the aerobicthreshold and is below the second intensity level.
 19. An apparatus,comprising: an input module configured to be communicatively coupled toa sensor configured to directly and non-invasively measure a lactateconcentration in a blood stream of a mammalian body during a physicalactivity; a training module communicatively coupled to the input module,the training module configured to calculate an exertion level based onthe direct and non-invasive measurement of lactate concentration; acomparison module configured to compare the lactate concentration to anaerobic threshold; and an output module communicatively coupled to thetraining module and the comparison module, the output module configuredto cause an output device to provide training feedback during thephysical activity based on the comparison of the lactate concentrationto the aerobic threshold.
 20. The apparatus of claim 19, wherein thetraining module is configured to calculate the exertion level basedsolely on the concentration of lactate.
 21. The apparatus of claim 19,wherein the training module is configured to calculate the exertionlevel independently of a heart rate of the mammalian body.
 22. Theapparatus of claim 19, wherein the comparison module is configured tocompare the lactate concentration to the aerobic threshold and ananaerobic threshold.
 23. The apparatus of claim 19, wherein theapparatus is configured to be coupled to the mammalian body during thephysical activity such that the input module receives a signal from thesensor while the apparatus is coupled to the mammalian body and theoutput module causes the output device to provide training feedbackwhile the apparatus is coupled to the mammalian body.
 24. The apparatusof claim 19, wherein the comparison module is configured to classify theexertion level as being within a first training zone, a second trainingzone, or a third training zone based on the direct and non-invasivemeasurement of lactate concentration, the first training zone associatedwith a first ratio of aerobic to anaerobic exertion below the aerobicthreshold, the third training zone associated with a third ratio ofaerobic to anaerobic exertion above the aerobic threshold and below ananaerobic threshold, and the second training zone associated with asecond ratio of aerobic to anaerobic exertion between the first ratioand the third ratio.
 25. The apparatus of claim 19, wherein: thetraining module is configured to define the aerobic threshold based on afirst inflection point in the lactate concentration; the training moduleis configured to define an anaerobic threshold based on a secondinflection point in the lactate concentration; the comparison module isconfigured to classify the exertion level as being within a firsttraining zone, a second training zone, or a third training zone based onthe direct and non-invasive measurement of lactate concentration, thefirst training zone associated with a first ratio of aerobic toanaerobic exertion below the aerobic threshold, the third training zoneassociated with a third ratio of aerobic to anaerobic exertion above theanaerobic threshold, and the second training zone associated with asecond ratio of aerobic to anaerobic exertion that exceeds the aerobicthreshold and is below the third ratio; and the output module isconfigured to cause the output device to provide training feedback toavoid the second training zone when the comparison module classifies theexertion level within the second training zone.
 26. A method,comprising: defining, with a training aid and during a first timeperiod, a training regimen prescribing (1) performing a physicalactivity performed at a first intensity level associated with a firstlactate concentration, (2) performing the physical activity at a secondintensity level associated with a second lactate concentration, and (3)avoiding performing the physical activity at a third intensity levelassociated with a third lactate concentration, the third lactateconcentration being between the first lactate concentration and thesecond lactate concentration; classifying, at the training aid andduring a second time period in which a user is engaged in the physicalactivity, the second time period after the first time period, a trainingzone for the physical activity based on a measured lactateconcentration; determining, during the second time period, that thetraining zone is associated with the third intensity level; and sendinga signal to an output device to cause the output device to provide aprompt to the user to alter the performance of the physical activityduring the second time period to avoid the third intensity level. 27.The method of claim 26, wherein the second time period has a duration ofless than thirty minutes.
 28. The method of claim 26, furthercomprising: receiving, during the second time period and beforeclassifying the training zone, a signal from a non-invasive sensorassociated with a direct measurement of the measured lactateconcentration.
 29. The method of claim 26, wherein the measured lactateconcentration is a first measured lactate concentration, the trainingzone is a first training zone, and the prompt is a first prompt toperform the physical activity at the first intensity level, the methodfurther comprising: receiving, during the second time period and aftersending the signal to the output device, a signal from a non-invasivesensor associated with a second measured lactate concentration;classifying, at the training aid and during the second time period, asecond training zone for the physical activity based on the secondmeasured lactate concentration; determining that the second trainingzone is a target training zone; and sending a signal to the outputdevice to cause the output device to provide a second prompt to maintaina second intensity of the physical activity.
 30. The method of claim 26,wherein the measured lactate concentration is a first measured lactateconcentration and the training zone is a first training zone, the methodfurther comprising: receiving, during the second time period, a signalfrom a non-invasive sensor associated with a second measured lactateconcentration, the second measured lactate concentration associated withthe first intensity level; classifying, at the training aid and duringthe second time period, a second training zone for the physical activitybased on the second measured lactate concentration; determining that thesecond training zone is a target training zone; and sending a signal tothe output device to cause the output device to display a timer countingdown a duration while the signal associated with the second measuredlactate concentration is received.
 31. The method of claim 26, whereinthe measured lactate concentration is a first measured lactateconcentration, the training zone is a first training zone, and thetraining regimen prescribes performing the physical activity at thefirst intensity level for a first duration and performing the physicalactivity at a second intensity level for a second duration, the methodfurther comprising: receiving during a third time period, a signal froma non-invasive sensor associated with a second measured lactateconcentration; classifying, at the training aid and during the thirdtime period, a second training zone for the physical activity based onthe second measured lactate concentration, the second training zoneassociated with the first intensity level; determining during the thirdtime period that the physical activity has been performed for the firstduration; and sending a signal to the output device to cause the outputdevice to prompt the user to alter the performance of the physicalactivity to conform with the second intensity level based on determiningthat the physical activity has been performed for the first duration.32. The method of claim 26, wherein the first lactate concentration isassociated with a ratio of aerobic to anaerobic exertion below anaerobic threshold and the second lactate concentration is associatedwith a ratio of aerobic to anaerobic exertion above an anaerobicthreshold, the method further comprising: directly and non-invasivelymeasuring the measured lactate concentration.
 33. The method of claim26, wherein the measured lactate concentration is a first measuredconcentration from a plurality of measured lactate concentrations, themethod further comprising: directly non-invasively measuring a pluralityof measured lactate concentrations including the measured lactateconcentration; defining an aerobic threshold based on a first inflectionpoint in the plurality of measured lactate concentrations; defining ananaerobic threshold based on a second inflection point in the pluralityof measured lactate concentrations, the first lactate concentrationapproaching but not exceeding the aerobic threshold, the second lactateconcentration approaching but not exceeding the anaerobic threshold, andthe third lactate concentration exceeding the aerobic threshold.